Random decision forests are ensembles of tree data structures which may be used for a variety of machine learning tasks. For example, classification tasks, prediction tasks involving regression problems, and detecting abnormalities. Random decision forest technology is useful in many application domains such as gesture recognition for natural user interfaces, computer vision, robotics, medical image analysis and others.
A decision forest is a plurality of decision trees each having a root node, a plurality of split nodes and a plurality of leaf nodes. Associated with each leaf node is data accumulated during a training phase when the decision forest is trained. During training the structure of the trees is learnt and tests are selected (from randomly generated tests in the case of random decision forests) for use at the split nodes. The data at the leaf nodes may be aggregated. For example, it may be represented using a probability distribution.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known machine learning systems which use random decision forests.